Massive multiple input, multiple output (MIMO) is proposed for use in 5G wireless networks. In contrast to base stations for conventional MIMO systems, base stations in massive MIMO systems are equipped with many more antennas (e.g., approximately 20 to 100 antennas or more in next generation systems). In massive MIMO, a larger number of users are served simultaneously using multiuser MIMO techniques. In massive MIMO, thermal noise and fast fading vanish. Massive MIMO also provides simplified multiuser processing, reduced transmit power, and high sum-rates.
Channel state information (CSI) is an important parameter in massive MIMO systems. The CSI is used on the uplink to separate users through receive beamforming and is used on the downlink to send different data to different users through transmit beamforming. One method for determining CSI is channel estimation. Currently, two methods are proposed for channel estimation in massive MIMO. One method utilizes fully random beams. The other utilizes a collection of pointy beams. Both have disadvantages. Performing channel estimation with fully random beams is not backward compatible with user equipment (UEs) that only support beam training. Performing channel estimation with a collection of point beams does not work as well as randomized beams in terms of channel estimation.